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Multimorbidity is a huge public health issue that affects people's health significantly on a global scale. Many chronic diseases have been linked to exposure to workplace dangers. The goal of this study was to comprehend the existing degree of multimorbidity among steel workers, the connection between cumulative occupational hazard exposure and multimorbidity, and to build the groundwork for the creation of preventative and control strategies for multimorbidity among steelworkers. This cross-sectional study included 3577 steel workers who had occupational health examinations in 2017. The relationship between occupational risks and the prevalence of multimorbidity in steel workers was examined using multiple logistic regression. We discovered that the rate of multimorbidity was 61.3%. The risk of multimorbidity among employees exposed to high levels of shift work, heat, noise, and dust was 1.520 (95% CI: 1.147 to 2.014), 3.376 (95% CI: 2.583 to 4.413), 2.069 (95% CI: 1.653 to 2.590), and 1.509 (95% CI: 1.1262.023) respectively in a multifactorial logistic regression analysis. Steelworkers need stronger protection against each occupational hazard since multimorbidity is highly common within the steel sector as a whole and is made more likely by occupational hazard exposure.

Background. Gastric cancer (GC) is the fifth most common malignant tumor and the third leading cause of cancer-related deaths. Because GC has the characteristics of high heterogeneity, unclear mechanism, limited treatment methods, and low five-year survival rate, it is necessary to find the prognostic biomarkers of GC and explore the mechanism of GC. Methods. We first identified differentially expressed genes (DEGs) between gastric cancer and normal gastric cells through expression analysis. A protein-protein interaction (PPI) network was constructed to find tightly connected modules. We performed survival analysis on the DEGs in the modules to identify genes with prognostic significance. Gene set enrichment analysis (GSEA) was used to identify gene enrichment pathways. Finally, we used our own collected clinical samples of 119 gastric adenocarcinoma (STAD) tissues and 40 normal gastric tissues to perform immunohistochemical (IHC) staining to verify the differential expression of COL8A1 in STAD tissues and normal gastric tissues and its correlation with epithelial-mesenchymal transition- (EMT-) related factors. Results. We identified 356 DEGs through differential expression analysis. Through PPI analysis and survival analysis, we determined that the collagen type VII alpha-1 chain (COL8A1) gene has prognostic significance. GSEA analysis showed that COL8A1 was significantly enriched in the EMT. IHC results showed that COL8A1 was upregulated in STAD tissues and could be used as an independent prognostic factor and was related to EMT. Conclusion. This study shows that COL8A1 is related to the prognosis of GC patients and might affect the progress of GC through the EMT pathway. Therefore, COL8A1 may be a biomarker for predicting the prognosis of GC.

Search algorithms based on discrete-time quantum walk (QW) can be considered as alterations of the standard QW: Use a different coin operator that distinguishes target and nontarget vertices, or, mark the target vertices first followed by the standard QW. Two most frequently used marking coins are [Formula: see text] and [Formula: see text] the negative identity operator and the negative Grover diffusion operator. We show that search algorithms corresponding to these four combinations can be reduced to two, denoted as [Formula: see text] and [Formula: see text], and they are equivalent when searching for nonadjacent multiple targets. For adjacent target vertices, numerical simulations show that the performance of the algorithm [Formula: see text] highly depends on the density of the underlying graph, and it outperforms [Formula: see text] when the density is large enough. At last, a generalized stationary state of both search algorithms on the graphs with even-numbered degree is provided.

Coal workers are more likely to develop chronic obstructive pulmonary disease due to exposure to occupational hazards such as dust. In this study, a risk scoring system is constructed according to the optimal model to provide feasible suggestions for the prevention of chronic obstructive pulmonary disease in coal workers. Using 3955 coal workers who participated in occupational health check-ups at Gequan mine and Dongpang mine of Hebei Jizhong Energy from July 2018 to August 2018 as the study subjects, random forest, logistic regression, and convolutional neural network models are established, and model performance is evaluated to select the optimal model, and finally a risk scoring system is constructed according to the optimal model to achieve model visualization. The training set results show that the logistic, random forest, and CNN models have sensitivities of 78.55%, 86.89%, and 77.18%; specificities of 85.23%, 92.32%, and 87.61%; accuracies of 81.21%, 85.40%, and 83.02%; Brier scores of 0.14, 0.10, and 0.14; and AUCs of 0.76, 0.88, and 0.78, respectively, and similar results are obtained for the test set and validation set, with the random forest model outperforming the other two models. The risk scoring system constructed according to the importance ranking of random forest predictor variables has an AUC of 0.842; the evaluation results of the risk scoring system shows that its accuracy rate is 83.7% and the AUC is 0.827, and the established risk scoring system has good discriminatory ability. The random forest model outperforms the CNN and logistic regression models. The chronic obstructive pulmonary disease risk scoring system constructed based on the random forest model has good discriminatory power.

The Chinese Visceral Adiposity Index (CVAI) is an indicator of visceral adiposity dysfunction used to evaluate the metabolic health of the Chinese population. Steelworkers are more likely to be obese due to their exposure to special occupational factors, and have a higher prevalence of carotid atherosclerosis (CAS). This study aimed to analyze the special relationship between CVAI and CAS among steelworkers. A total of 4075 subjects from a northern steel company were involved in the cross-sectional study. Four logistic regression models were developed to analyze the correlation between CVAI and CAS. In addition, the restricted cubic spline was applied to fit the dose–response association between CVAI and CAS risk. In the study, the prevalence of CAS was approximately 25.94%. After adjustment for potential confounders, we observed a positive correlation between CVAI and CAS risk. Compared to the first CVAI quartile, the effect value odds ratio (OR) and 95% CI in the second, third, and fourth CVAI quartile were 1.523 (1.159–2.000), 2.708 (2.076–3.533), and 4.101 (3.131–5.372), respectively. Additionally, this positive correlation was stable in all subgroups except for female. Furthermore, we also found a non-linear relationship between CVAI and CAS risk (p nonlinear < 0.05). Notably, CVAI could increase the risk of CAS when higher than 106. In conclusion, our study showed that CVAI might be a reliable indicator to identify high-risk populations of CAS among steelworkers.

Additional file 4: Table S1. Univariate Cox regression and multivariate Cox regression analysis of the factors associated with recurrent hemoptysis of patients with idiopathic bronchiectasis after BAE. Table S2. Univariate Cox regression and multivariate Cox regression analysis of the factors associated with recurrent severe hemoptysis of patients with idiopathic bronchiectasis after BAE.

Additional file 3: Figure S3. Specific origination, the recurrence rate, and the severe recurrence rate of abnormal AbBAs on CTA. (A) The proportion of certain abnormal AbBAs on CTA to all abnormal AbBAs on CTA. (B) Ratio of recurrent hemoptysis group to all patients per abnormal AbBAs on CTA. (C) Ratio of recurrent severe hemoptysis group to all patients per abnormal AbBAs on CTA.

Additional file 1: Figure S1. The cumulative rates of patients with recurrent hemoptysis-free or recurrent severe hemoptysis-free with Pseudomonas aeruginosa, abnormal AbBAs on CTA, extensive bronchiectasis, and high 24-h sputum volume.

Additional file 2: Figure S2. Distribution characteristics of abnormal arteries in 588 patients. BAs, bronchial arteries; NBSAs, non-bronchial system arteries.

Respiratory syncytial virus (RSV) is a major pathogen that can cause acute respiratory infectious diseases of the upper and lower respiratory tract, especially in children, elderly individuals, and immunocompromised people. Generally, following viral infection, respiratory epithelial cells secrete cytokines and chemokines to recruit immune cells and initiate innate and/or adaptive immune responses. However, whether chemokines affect viral replication in nonimmune cells is rarely clear. In this study, we detected that chemokine CCL5 was highly expressed, while expression of its receptor, CCR1, was downregulated in respiratory epithelial cells after RSV infection. When we overexpressed CCR1 on respiratory epithelial cells

Chemistries of Nb(V) and Ta(V) compounds are essentially identical as a result of lanthanide contraction. Hydrolysis of M(NMe

Objective: To process and extract electrocardiogram (ECG, ECG, or EKG) features using a convolutional neural network (CNN) to establish an ECG-assisted diagnosis model. Methods: Coal workers who underwent physical examinations at Gequan Mine Hospital and Dongpang Mine Hospital of Hebei Jizhong Energy from July 2020 to September 2020 were selected as the study subjects. The ECG images were preprocessed. We use Python software and convolutional neural network to establish ECG images recognition and classification model.We usecalibration curve, calibration-in-the-large, Brier score, specificity, sensitivity, F1 score, Kappa value, accuracy, and area under the curve (AUC) of ROC to evaluate the performance of the model. Results: The number of abnormal ECG results was 849, and the rate of abnormal results was 25.02%. The test set accuracies of the sinus bradycardia model, nonspecific intraventricular conduction delay model, myocardial ischemia model, and sinus tachycardia model were 97.66%, 96.49%, 93.62%, and 93.02%, respectively; sensitivities were 96.63%, 96.30%, 96.88% and 95.24%, respectively; specificities were 98.78%, 96.67%, 86.67%, and 90.90%, respectively; Brier scores were 0.03, 0.07, 0.09, and 0.11, respectively; Calibration-in-the-large values were 0.026, 0.110, 0.041, and 0.098, respectively. Conclusions: The convolutional neural network model can accurately identify the main ECG abnormality types of coal workers. Additionally, the main ECG abnormalities in these coal company workers were sinus bradycardia, non-specific intraventricular conduction delay, myocardial ischemia, and sinus tachycardia.